How to use Outlier Detector
- Enter the numeric observations and choose IQR or z-score.
- Set the multiplier or threshold and the supported variance convention.
- Review the threshold report and export all observations with their flags.
Example: Outlier Detector
Outlier Detector: 1 of 9 values is a possible outlier (IQR method, 1.5 x IQR).
Options
- Detection rule
- Choose the supported IQR or z-score rule and its threshold. They describe different ways of flagging unusual entered values.
- Flagged observations
- Review the original row or position with each flag. A flagged value stays part of the source and is not automatically corrected or deleted.
Supported inputs and limits
Where your input is processed
This tool processes your input in this browser. Your text and files are not uploaded to UseFreeTools. Check this tool's limits for anything it may save on your device.
An unusual value may still be valid
A flag can reflect a recording error, a real extreme event or a model that does not fit the data. Check units, collection conditions and the shape of the distribution before deciding what to do. Repeating the analysis with a different threshold does not by itself resolve the cause.
Questions about Outlier Detector
Does a flagged value get deleted?
No. All observations remain in their original order with an added flag. Review the context before changing a dataset.
Why can IQR and z-score flag different values?
They use different summaries and assumptions. Extreme values can affect a mean and standard deviation, while quartile fences depend on the ordered distribution.
Can I use z-scores on a constant list?
Its standard deviation is zero, so ordinary z-scores are undefined. The tool states that case rather than dividing by zero.